Methodology of above: - only had population for total residential (includes mutli family, etc) - but had SF data for single family homes. got percentage that single family homes make up of total residential, to multiply by the usage per capita for total residential to get usage per capita for single family homes - helps to control for neighborhoods that might have different proportions of homes vs. multifamily complexes, mobile homes, etc MAP: ECPH.
##
## Call:
## lm(formula = energy_intensity ~ weighted_avg_income, data = income_engint_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44713 -3855 1088 5553 47175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.947e+04 9.254e+02 64.27 <2e-16 ***
## weighted_avg_income -1.693e-01 8.882e-03 -19.06 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10590 on 1382 degrees of freedom
## Multiple R-squared: 0.2081, Adjusted R-squared: 0.2075
## F-statistic: 363.2 on 1 and 1382 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = usage_percap ~ weighted_avg_income, data = income_ECPC_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21559589 -4207766 227923 4259318 58316276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.801e+05 6.309e+05 -0.761 0.447
## weighted_avg_income 1.833e+02 6.052e+00 30.292 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7182000 on 1364 degrees of freedom
## Multiple R-squared: 0.4022, Adjusted R-squared: 0.4017
## F-statistic: 917.6 on 1 and 1364 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = ecph ~ owned_units_perc, data = ECPH_vs_TENURE_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39261390 -7242051 -1431296 4866181 202095189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28578202 1101564 25.94 <2e-16 ***
## owned_units_perc 56948772 1733185 32.86 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13850000 on 1367 degrees of freedom
## Multiple R-squared: 0.4413, Adjusted R-squared: 0.4409
## F-statistic: 1080 on 1 and 1367 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = ecpc ~ owned_occ_perc, data = ECPC_vs_TENURE_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20360763 -3875489 -271107 2890330 64527492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1996103 517724 -3.856 0.000121 ***
## owned_occ_perc 32557036 808282 40.279 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6278000 on 1364 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5433, Adjusted R-squared: 0.5429
## F-statistic: 1622 on 1 and 1364 DF, p-value: < 2.2e-16
The above (2) plots show that increased ownership is associated with increased consumption, both when measured per household and per occupant
##
## Call:
## lm(formula = energy_intensity ~ owned_units_perc, data = EISF_vs_TENURE_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44493 -4502 1289 5325 54132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51485.6 914.1 56.33 <2e-16 ***
## owned_units_perc -14675.9 1436.1 -10.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11480 on 1382 degrees of freedom
## Multiple R-squared: 0.07026, Adjusted R-squared: 0.06959
## F-statistic: 104.4 on 1 and 1382 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = ecph ~ weighted_avg_value, data = ECPH_vs_VALUE_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50599004 -9983583 -115997 8491055 199325888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.219e+07 1.135e+06 37.16 <2e-16 ***
## weighted_avg_value 3.463e+01 1.771e+00 19.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16360000 on 1366 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2188, Adjusted R-squared: 0.2182
## F-statistic: 382.6 on 1 and 1366 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = ecph ~ weighted_avg_years_in_home, data = ECPH_vs_YEARS_in_home_2013)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37873158 -8410277 -2560795 4795491 204376242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9797196 2399111 4.084 4.69e-05 ***
## weighted_avg_years_in_home 2568290 114773 22.377 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15850000 on 1367 degrees of freedom
## Multiple R-squared: 0.2681, Adjusted R-squared: 0.2676
## F-statistic: 500.7 on 1 and 1367 DF, p-value: < 2.2e-16
Equity analysis in this case is tricky, since consumption data from the utility (PG&E) was, for privacy reasons, aggregated to the census tract level as the smallest geographical granularity. This means that we can compare a few census tracts by their overall efficiency scores (EISF for single family homes) and their overall racial makeup, but we cannot say for certain which specific houses operate more or less efficiently and who exactly lives in those houses. So, the tracts with both the highest and the lowest EISF scores (shown in the EISF maps) will be inspected for their racial make up to see if any trends are apparent.
As we can see above..
Scrap Code Below: